R-squared is the percentage of the response variable variation that is explained by a linear model. It is always between 0 and 100%. R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.
In general, the higher the R-squared, the better the model fits your data. However, there are important conditions for this guideline that I discuss elsewhere. Before you can trust the statistical measures for goodness-of-fit, like R-squared, you should check the residual plots for unwanted patterns that indicate biased results.